RevOps Lab

Ravi Parimi has spent 20+ years in revenue operations and GTM strategy, from AWS, VMware and Cisco to Zendesk and ServiceTitan, plus a stint as executive in residence advising a16z portfolio companies. He joins Janis and Philipp to explain why the agent-first mindset breaks in production and why workflows, not agents, are the unit of design. Together they dig into human-agent handoffs, permission scopes and guardrails, and why RevOps has to run agents like a product after launch. Ravi closes with the unpopular opinion that most teams badly underestimate the operating model change ahead.We cover:- Why an agent-first mindset works for experimenting but fails in production- Workflow as the unit of design: inputs, outputs, people and systems- Lead routing as the test case: account disputes, strategic accounts and other edge cases- Why deterministic rules matter if you want agents without hallucinations- Treating agents as digital workers in the talent mix: what agents do best vs. humans- Autonomy with a scope, like a driver's license- How agent speed changes your org design and the humans in the loop- Running agents like a product: roadmap, workflow v1/v2/v3, benchmarks and release cycles- Agent ops: owning speed, quality, cost and latency after launch- Why agents need sandboxes, testing and security layers before production- Why modern revenue organizations will run as systems of agents and humans- Why solution and field engineering leaders could be tomorrow's CROs- Why judgment and taste become the premium skill when agents give the answersRavi Parimi: https://www.linkedin.com/in/rparimi/ Janis Zech: https://www.linkedin.com/in/janiszech/ Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Chapters:(00:03) Ravi Parimi's path through RevOps(02:59) What's wrong with agent-first(06:54) Jobs to be done and lead routing(09:56) Why the workflow mindset is missing(13:54) Agents as digital workers(15:44) Autonomy, scope and permissions(17:13) Rethinking workflows and org design(18:58) Running agents like a product(21:11) Testing, sandboxes and guardrails(24:13) The future of RevOps and the CRO(28:48) Field engineers as future CROs(32:01) Judgment, taste and revenue architecture(34:26) Unpopular opinion: operating model

Janis Zech, CEO and founder of WeFlow, joins Kelly Lewis and Tiffany Jones on The Revenue Insiders to explain why AI in go-to-market fails without a clean data foundation. He walks through automated activity capture, AI meeting prep and coaching scorecards on MEDDIC/MEDDPICC, and why accurate forecasts depend on deal intelligence. The hosts push back on critical thinking, business context and vibe coding. Janis leaves listeners with one question for annual planning: are you AI-first or problem-first?We cover:- The three problems behind weak AI results in go-to-market: siloed data, non-selling time and unpredictable quota attainment- Why manual data entry doesn't work and what to automate instead- Design principles for data capture: integrate the signal systems, map everything back to the CRM, stay headless- Why activity has to be mapped to opportunities, not just accounts- AI meeting prep that cuts 15 minutes to 5 and lives in the calendar, Slack or email- Coaching scorecards that auto-populate MEDDIC/MEDDPICC from every email, call and deal room- Automation vs. human in the loop: why reps still need to understand the framework- Why enablement and critical thinking matter more once AI does the data entry- From system of record to system of intelligence to engagement orchestration- Why AI forecasting failed without data pipelines, and how to give CROs multiple forecast numbers- Feeding AI your business context: personas, industries, buying cycles- Mining transcripts and emails for the real close-lost reasons- Why vibe-coding your own GTM stack is slower and costlier than it looksOriginal episode: https://www.therevenueinsiders.com/podcast/episode/39359e05/ai-first-or-problem-first-the-revops-question-for-2027 Kelly Lewis: https://www.linkedin.com/in/kellylewis418 Tiffany Jones: https://www.linkedin.com/in/tiffanyelizabethjonesThe Revenue Insiders: https://www.therevenueinsiders.com Janis Zech: https://www.linkedin.com/in/janiszech Philipp Stelzer: https://www.linkedin.com/in/philippstelzer WeFlow: https://www.weflow.ai Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Chapters:(00:01) Janis's path to WeFlow(03:43) Three problems in go-to-market(05:48) What's different from Gong and Clari(07:08) Design principles for data capture(12:55) AI coaching on MEDDPICC(15:40) Automation vs. human in the loop(19:45) Enablement in an AI world(23:34) System of intelligence(24:52) Forecasting needs deal intelligence(26:48) Feeding AI your business context(30:58) Mining transcripts for real answers(32:14) Vibe coding vs. long-term partnership(35:07) AI-first or problem-first?

Avtar Varma, who just started as VP RevOps at Chainguard after building RevOps functions from Series A startups to Stripe, lays out three stages of enterprise AI adoption: token maxing, centralizing within functions, and a company-wide AI ops team. He explains why usage leaderboards are a vanity metric and what to reward instead. Together with Janis and Philipp, he debates where an AI ops function should sit, who owns the data infrastructure, and why go-to-market is the best place to prove ROI. He closes with an unpopular take on go-to-market engineers.We cover:- Stage one: token maxing, and why "give everyone every AI tool" makes ROI hard to prove- Why AI usage leaderboards are a vanity metric- Hackathons vs. leaderboards: rewarding the best ideas, not the biggest spend- Meeting prep as a simple, high-impact use case, and why AI has to live in the calendar- Stage two: centralizing skills, templates and agents inside each function- Using usage data to coach below-average adopters instead of rewarding top spenders- Stage three: a central AI ops team with a liaison for each function- Where AI ops should sit: COO, CIO or RevOps, and who owns data infrastructure- Saving cost with open-source models where best-in-class models aren't needed- Why the data warehouse is becoming the layer agents run on- Whether RevOps can grow into BizOps or AI ops- Why go-to-market is the best place to start proving AI ROI- Unpopular opinion: go-to-market engineers should be actual engineersAvtar Varma: https://www.linkedin.com/in/avtar-varmaChainguard: https://www.chainguard.dev/Janis Zech: https://www.linkedin.com/in/janiszech/Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/WeFlow: https://www.weflow.ai/Community: https://www.weflow.ai/communityRevOps Letter: https://www.weflow.ai/revops-letterChapters:(00:01) Avtar Varma joins from Chainguard(01:58) Three stages of AI adoption(05:54) Is token maxing necessary?(07:33) Leaderboards as a vanity metric(10:33) Hackathons and meeting prep(14:46) Incentives that actually work(16:48) Coaching with usage data(18:10) Stage three: central AI ops(21:07) Who owns data and infrastructure(22:17) Cost, open-source models, security(24:46) Can RevOps become AI ops?(27:46) Why GTM is the place to start(28:31) Unpopular opinion: GTM engineers

Colin Gerber, VP RevOps & Strategy at Socure, returns for part two to unpack how his team forecasts revenue in a fully consumption-based business. He walks through the solution readiness document that substantiates every booking, the "BAR" (booked ARR realization) process that tracks ramp against reality, and why RevOps should own the forecasting system but not its accuracy. Plus: the comp structures that keep AEs and CSMs honest, and the two mistakes that blow the biggest holes in a consumption forecast.We cover:- How Socure prices ~35 modules by API-call consumption, not seats- Giving customers real-time visibility into what they're actually spending- The solution readiness document that turns a sales pitch into a forecastable number- Why AEs still call a bookings number — and when it gets challenged- Booking conservatively for pre-launch startups vs. mature, high-volume customers- Modeling seasonality into ramp schedules (BNPL, gaming, tax season)- Using live trials to close the gap between closed-won and go-live- The monthly BAR review with FP&A- Salesforce, JIRA, NetSuite and Tableau: the four-source forecasting stack- Booking discipline vs. realization — why bookings still matter in consumption pricing- Who actually owns the ramp: AEs, CSMs, and post-sales solution consultants- How 24-month payout schedules shape AE behavior on ramping accounts- The two mistakes that blow up a consumption-based forecastColin Gerber: https://www.linkedin.com/in/colinsgerber/Socure: https://www.socure.com/Janis Zech: https://www.linkedin.com/in/janiszech/Philipp Stelzer: https://www.linkedin.com/in/philippstelzer/WeFlow: https://www.weflow.ai/Community: https://www.weflow.ai/communityRevOps Letter: https://www.weflow.ai/revops-letterChapters:(00:01) Colin Gerber returns from Socure(02:37) How Socure prices consumption(06:08) Real-time visibility into spend(09:07) Why consumption pricing is spreading(10:49) Forecasting sessions & the solution readiness doc(15:07) Contract terms and seasonality(17:41) Live trials close the go-live gap(20:05) Ramp realization and revenue recognition(21:55) The four-source forecasting stack(25:05) Booking discipline vs. realization(29:14) Who owns the ramp(34:41) Two mistakes that wreck a forecast(39:07) RevOps shouldn't own forecast accuracy

Nicole Bradshaw spent six months as interim global sales leader at Eventbrite — and discovered how much she didn't know about her own job. She joins Janis to explain why RevOps teams know exactly what good selling looks like but never apply it to themselves, and why an order-taking RevOps function is the one most easily automated away.We cover:What changed when Nicole became her own stakeholderWhy execs only want your top two problems, not all of themBalancing business impact against time to impactUsing OKRs as a vehicle, not a solutionMatching operating cadence to stakeholder levelStrong opinions held loosely — showing up with a point of viewWhy order-taking RevOps is the easiest role to automateBridging strategy and execution as RevOps' unique positionMulti-threading inside your own companyPicking the right champion for the right initiativeObjection handling: anticipate, prepare, bring dataMaking people's lives easier before you need favorsFraming every ask as a win-winNicole Bradshaw on LinkedIn: https://www.linkedin.com/in/nkbradshaw/ PandaDoc: https://www.pandadoc.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letterResource recommendation: Harvard Business Review — Nicole's habit of blocking 30 minutes every other week for professional development readingChapters: (00:00) Intro & welcome to Nicole (00:37) Nicole's path into RevOps (03:08) Six months leading sales (04:43) Becoming your own stakeholder (07:34) Impact vs. time to impact (09:28) OKRs and operating cadence (13:08) Showing up with agency (17:19) AI-proofing the RevOps role (18:26) Bridging strategy and execution (22:56) Multi-threading and champions